{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 401 CNN\n",
    "\n",
    "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n",
    "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n",
    "\n",
    "Dependencies:\n",
    "* torch: 0.1.11\n",
    "* torchvision\n",
    "* matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "import torch.utils.data as Data\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f0668162930>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(1)    # reproducible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Hyper Parameters\n",
    "EPOCH = 1               # train the training data n times, to save time, we just train 1 epoch\n",
    "BATCH_SIZE = 50\n",
    "LR = 0.001              # learning rate\n",
    "DOWNLOAD_MNIST = True   # set to False if you have downloaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Mnist digits dataset\n",
    "train_data = torchvision.datasets.MNIST(\n",
    "    root='./mnist/',\n",
    "    train=True,                                     # this is training data\n",
    "    transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to\n",
    "                                                    # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n",
    "    download=DOWNLOAD_MNIST,                        # download it if you don't have it\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([60000, 28, 28])\n",
      "torch.Size([60000])\n"
     ]
    },
    {
     "data": {
      "image/png": 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ak2tfd6P1Vdd6qyP8r0g6zfa3bR8l6fuS1tTQxzfYntg4ESPbEyV9T/039PgaSQsbzxdK\neqLGXv5Ivwzb3mxYedW87vpuuPuI6PlD0mUaPuP/O0n/UEcPTfqaIen1xuOtunuT9LCGdwP/V8Pn\nRm6U9GeSnpf0rqT/lDS5j3r7dw0P5f6GhoM2tabe5mp4l/4NSRsbj8vqXneFvmpZb9zeCyTFCT8g\nKcIPJEX4gaQIP5AU4QeSIvxAUoQfSOr/AH6evjIXWuv8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0621c6b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot one example\n",
    "print(train_data.train_data.size())                 # (60000, 28, 28)\n",
    "print(train_data.train_labels.size())               # (60000)\n",
    "plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\n",
    "plt.title('%i' % train_data.train_labels[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\n",
    "train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert test data into Variable, pick 2000 samples to speed up testing\n",
    "test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)\n",
    "test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000]/255.   # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)\n",
    "test_y = test_data.test_labels[:2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1 = nn.Sequential(         # input shape (1, 28, 28)\n",
    "            nn.Conv2d(\n",
    "                in_channels=1,              # input height\n",
    "                out_channels=16,            # n_filters\n",
    "                kernel_size=5,              # filter size\n",
    "                stride=1,                   # filter movement/step\n",
    "                padding=2,                  # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1\n",
    "            ),                              # output shape (16, 28, 28)\n",
    "            nn.ReLU(),                      # activation\n",
    "            nn.MaxPool2d(kernel_size=2),    # choose max value in 2x2 area, output shape (16, 14, 14)\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(         # input shape (1, 28, 28)\n",
    "            nn.Conv2d(16, 32, 5, 1, 2),     # output shape (32, 14, 14)\n",
    "            nn.ReLU(),                      # activation\n",
    "            nn.MaxPool2d(2),                # output shape (32, 7, 7)\n",
    "        )\n",
    "        self.out = nn.Linear(32 * 7 * 7, 10)   # fully connected layer, output 10 classes\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = x.view(x.size(0), -1)           # flatten the output of conv2 to (batch_size, 32 * 7 * 7)\n",
    "        output = self.out(x)\n",
    "        return output, x    # return x for visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN (\n",
      "  (conv1): Sequential (\n",
      "    (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (1): ReLU ()\n",
      "    (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n",
      "  )\n",
      "  (conv2): Sequential (\n",
      "    (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (1): ReLU ()\n",
      "    (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n",
      "  )\n",
      "  (out): Linear (1568 -> 10)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "cnn = CNN()\n",
    "print(cnn)  # net architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)   # optimize all cnn parameters\n",
    "loss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.0623 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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J7l8BVPMM1c9b8BRR/HlMQVVCP9uiLqhC//4cEuPWUyHVHE3Qq5etMstGyM87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\n8jrZyoj1vMe+nEgRJVQz0O4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e3fe60f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.0904 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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6WyqiinJAi2vqCh/n+C1iMlEpCDG2wye8HCthN92YsZVRgD4GqY8fml0MLROT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\nvks+W71lzGZQFTmZ6CSAv//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IkM3v9boaQerH+E3e2hBem1/fxPz6Jt46ZmcamlsY9sLC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\n1ye57QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e34086d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.1173 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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WrOR7hwxEMpRhTpJMyvKC64FvAkdhrYeuwWBwhhsz/hhwqVJqX2AM8GMR2Re4CnhJKTUU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\nulJJLxO7Fr69Iq8E08rnClPdITfQVOl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e3e712b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.0559 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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GvwgJw8gAbCrPbJS9pPT5MRqmYwM6L0/d1VXQmUQ72cKr3MaFLKCRdcxhIpN5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\n1k/p4EOyVvd6vVdUSWf0nVw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e38dce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.0859 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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VxmZjPcHJQAToAfwxg3bc0xu2elwkXO5gOUg7/rBipzftzGTZtEa8/frT9zfd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\nSC0meaZesearaTRtX+sqDdP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KnzBo+F4AHHH+URx9ybHt9i9deXvScwsLy9lvwLU+Wqdx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\nQzt+jUajyTP+PylDpLvHb7ZhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e34601d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.0169 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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+dxXWaCqmMKmz2GuHrsN2UqV72XtXO7ajEC2NphFM0BjHbygpog7fVDRanrQ9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\nnXWZ6+O9koQ0TL+I3PGP+2a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SHQXOZTAURKXa2XDDZHoc8yMqB+8StTndyJcGmZsl4zbz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e3460470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.0625 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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613LiDafQY2DHZMq7M/hZ8j3pppJqvzdBMYV/lsa+psRYtNK9p6qf1beXrNWC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\nPUHLD7kyO81fdBVc/cOs18U2bGXP6//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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e37c2a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.0136 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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eufO88IfpH1ue92tmn2t4EVoZtCgduxjDn8/M/ByaNQp1nDzE12E4mRVWlTXr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e341c518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.0033 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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XdBe64DZjJ23+vhF9gwZMjD9INC7GenTOMs4+bFdb+4Y1NBMGMuXeG9yjo2ayQR9G+AuA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\nknkG3nPaZ+/67tbMHvASiz/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f061cc504a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.1449 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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VPHTRvdQ21CIinP2z8z3f61XQ41hVKfGTOLV75W8nsmH9vxi+xzf5xL7+f8nU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\nm/Oc1xVBUrttFYpEylW6OZt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e393f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.0121 | test accuracy: 0.98\n"
     ]
    },
    {
     "data": {
      "image/png": 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po5VGVmastnU63g5B32z9xqnTjy2eX8w7gduSCuP4PXDAQeNZu+p1mptW+HYN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e3c28710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | train loss: 0.0233 | test accuracy: 0.99\n"
     ]
    },
    {
     "data": {
      "image/png": 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nFUJf3VScaLt4VX6cQh/6kameYlSXd3bTPBP4pfljRSEodSZCcFfP+VHYpmTF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\ndz0wCegAphhjXvJoqydSF4UnTm53lR3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f05e3ca88d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# following function (plot_with_labels) is for visualization, can be ignored if not interested\n",
    "from matplotlib import cm\n",
    "try: from sklearn.manifold import TSNE; HAS_SK = True\n",
    "except: HAS_SK = False; print('Please install sklearn for layer visualization')\n",
    "def plot_with_labels(lowDWeights, labels):\n",
    "    plt.cla()\n",
    "    X, Y = lowDWeights[:, 0], lowDWeights[:, 1]\n",
    "    for x, y, s in zip(X, Y, labels):\n",
    "        c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)\n",
    "    plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)\n",
    "\n",
    "plt.ion()\n",
    "# training and testing\n",
    "for epoch in range(EPOCH):\n",
    "    for step, (x, y) in enumerate(train_loader):   # gives batch data, normalize x when iterate train_loader\n",
    "        b_x = Variable(x)   # batch x\n",
    "        b_y = Variable(y)   # batch y\n",
    "\n",
    "        output = cnn(b_x)[0]               # cnn output\n",
    "        loss = loss_func(output, b_y)   # cross entropy loss\n",
    "        optimizer.zero_grad()           # clear gradients for this training step\n",
    "        loss.backward()                 # backpropagation, compute gradients\n",
    "        optimizer.step()                # apply gradients\n",
    "\n",
    "        if step % 100 == 0:\n",
    "            test_output, last_layer = cnn(test_x)\n",
    "            pred_y = torch.max(test_output, 1)[1].data.squeeze()\n",
    "            accuracy = sum(pred_y == test_y) / float(test_y.size(0))\n",
    "            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\n",
    "            if HAS_SK:\n",
    "                # Visualization of trained flatten layer (T-SNE)\n",
    "                tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "                plot_only = 500\n",
    "                low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :])\n",
    "                labels = test_y.numpy()[:plot_only]\n",
    "                plot_with_labels(low_dim_embs, labels)\n",
    "plt.ioff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 2 1 0 4 1 4 9 5 9] prediction number\n",
      "[7 2 1 0 4 1 4 9 5 9] real number\n"
     ]
    }
   ],
   "source": [
    "# print 10 predictions from test data\n",
    "test_output, _ = cnn(test_x[:10])\n",
    "pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n",
    "print(pred_y, 'prediction number')\n",
    "print(test_y[:10].numpy(), 'real number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
